Meta-analysis of diagnostic test accuracy
is an important statistical tool for
summarizing test accuracy from multiple primary diagnostic studies
and is useful to
provide evidence on how well
the test discriminates people
with the target condition or disease from people without.
The test accuracy from primary studies are
usually reported in pairs of sensitivity and specificity,
dependent on the study-specific cutoff values
defining test positives and test negatives.
These study-specific cutoff values
induce
heterogeneity in test accuracy across multiple studies;
thus,
it is inappropriate to
pool
sensitivity or specificity separately
regardless of the cutoff values
in the meta-analysis
[1]}, [2]}.
Alternatively,
summary receiver operating characteristic curve (SROC) is recommended
to synthesize the reported pairs of sensitivity and specificity
[3]}, [4]}, [5]}, [6]}, [7]}, [2]}.
SROC presents the relationship between
sensitivity (true positive rate, TPR)
and
one minus specificity (false positive rate, FPR)
across studies,
depicting test accuracy at all possible cutoff values by a monotonic curve.
A natural candidate summary is the area under SROC (SAUC),
giving the average of TPR over all possible values of FPR
[9]}.
Two bivariate models are often applied to estimate SROC or SAUC.
One is
bivariate binomial model
(hierarchical SROC or HSROC model)
for pairs of TPR and FPR,
based on the ordinal logistic regression
with Bayesian inference
[4]}
or maximum likelihood estimation
[5]}.
The other is
bivariate normal model
for pairs of logit-transformed sensitivity and specificity,
based on asymptotic bivariate normality of empirical sensitivity and specificity
[6]}.
